Gastos Casa
08 September, 2025
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 26/4/2025 | Comida | 55874 | Tami | Supermercado |
| 28/4/2025 | Comida | 13050 | Tami | Cervezas MUT |
| 29/4/2025 | Electricidad | 52507 | Andrés | enel |
| 29/4/2025 | Diosi | 11990 | Andrés | arena 7kg superzoo |
| 3/5/2025 | Agua | 17072 | Andrés | aguas andina |
| 13/5/2025 | VTR | 22000 | Andrés | NA |
| 17/5/2025 | Electricidad | 52404 | Andrés | NA |
| 13/6/2025 | VTR | 22000 | Andrés | NA |
| 22/6/2025 | Electricidad | 52401 | Andrés | NA |
| 27/7/2025 | Electricidad | 52000 | Andrés | NA |
| 27/7/2025 | Comida | 59147 | Tami | Supermercado |
| 29/7/2025 | Comida | 10000 | Andrés | complemento comida |
| 29/7/2025 | Electrodomésticos/mantención casa | 68000 | Andrés | NA |
| 30/7/2025 | Comida | 24140 | Tami | Supermercado |
| 31/7/2025 | Gas | 19100 | Andrés | NA |
| 3/8/2025 | Comida | 86089 | Tami | Supermercado |
| 10/8/2025 | Comida | 108649 | Tami | Supermercado |
| 12/8/2025 | Enceres | 13490 | Tami | Confort |
| 16/8/2025 | VTR | 22000 | Andrés | NA |
| 17/8/2025 | Comida | 72586 | Tami | Supermercado |
| 20/8/2025 | Electricidad | 65242 | Andrés | 49393- 42306 -47872= 1521 |
| 25/8/2025 | Comida | 35500 | Andrés | ida al super |
| 25/8/2025 | Comida | 35000 | Andrés | piwen |
| 27/8/2025 | Comida | 37314 | Tami | Supermercado |
| 30/8/2025 | Comida | 67203 | Tami | Supermercado |
| 31/8/2025 | Diosi | 28000 | Andrés | american litter |
| 1/9/2025 | Comida | 7800 | Andrés | piwen |
| 7/9/2025 | Comida | 93538 | Tami | Supermercado |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 1.0224e+09 2 5.312 0.0051 **
## lag_depvar 2.6333e+11 1 2736.419 <2e-16 ***
## Residuals 8.2279e+10 855
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 -1735.885 16193.56 0.1412403
## 2-0 31330.491 23269.104 39391.88 0.0000000
## 2-1 24101.652 19442.059 28761.25 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 850 68503.71 2 56125.29
## 851 62945.00 2 68503.71
## 852 50209.71 2 62945.00
## 853 49436.29 2 50209.71
## 854 55308.00 2 49436.29
## 855 62650.86 2 55308.00
## 856 55683.00 2 62650.86
## 857 49762.14 2 55683.00
## 858 51835.00 2 49762.14
## 859 49806.43 2 51835.00
## 860 52799.57 2 49806.43
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 703 53564.75 21818.063
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57 61025.43 43913.86 46099.29 44524.86
## [764] 42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29 85493.71 74721.57 36232.14 40161.71 40629.86 45663.71
## [778] 39252.29 39618.57 39438.43 44650.71 38626.71 38280.43 44134.14
## [785] 47596.43 45598.43 42564.29 45699.14 49553.86 50018.43 43772.86
## [792] 39235.43 39905.00 40374.43 34230.57 34324.14 33491.57 33366.43
## [799] 46646.86 49770.86 57339.86 59799.14 53577.14 61775.29 70627.86
## [806] 57888.43 49960.71 42923.71 47284.86 52284.86 50191.00 36465.86
## [813] 34525.14 43199.14 52757.43 43200.86 36772.29 29568.00 42362.00
## [820] 42566.29 39596.00 32925.00 43416.57 52624.86 57733.71 54120.57
## [827] 53353.43 56286.86 60626.86 61375.29 53710.86 55795.57 55130.14
## [834] 57700.14 61333.14 59230.71 49195.00 55436.43 50353.14 43194.86
## [841] 47539.71 35271.00 34774.86 48788.71 50717.71 51727.43 51313.14
## [848] 56125.29 68503.71 62945.00 50209.71 49436.29 55308.00 62650.86
## [855] 55683.00 49762.14 51835.00 49806.43 52799.57
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [815] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [852] 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6
## 2013.397412 4038.019974 -535.702244 2440.125821 -2964.945956
## 7 8 9 10 11
## 520.449201 -5653.344202 -1190.524905 -3969.134683 -423.840444
## 12 13 14 15 16
## -4944.738162 -1617.958804 -908.229762 369.746603 -3248.729236
## 17 18 19 20 21
## -385.553139 -2136.605945 6596.972954 -1528.849871 -1208.960745
## 22 23 24 25 26
## 1474.371468 -1185.822102 234.697092 1695.769617 -7099.261049
## 27 28 29 30 31
## 943.898611 8190.725379 425.147450 -6.893761 -2393.762313
## 32 33 34 35 36
## 1580.518434 4578.395391 1137.033395 2402.006360 -1855.850628
## 37 38 39 40 41
## 4617.432789 4309.363632 -2266.368253 -2975.346235 -1107.569158
## 42 43 44 45 46
## -10740.189282 7280.366890 2556.297769 1368.459720 8107.911841
## 47 48 49 50 51
## 696.256870 6538.302738 6728.963757 -5863.193537 -4784.179703
## 52 53 54 55 56
## -5054.334393 -7928.549707 6122.420782 -4077.254288 -4898.686475
## 57 58 59 60 61
## 3847.421311 884.156159 -34.468025 140.168159 -4998.064982
## 62 63 64 65 66
## 18120.686993 3652.447067 -3632.302641 5934.103230 7357.393923
## 67 68 69 70 71
## 14657.640935 1723.593048 -13184.353327 -1293.528564 4653.493554
## 72 73 74 75 76
## -4887.048584 -4397.378218 -10495.105883 2459.477667 -5403.644975
## 77 78 79 80 81
## 1055.648235 -6872.167536 535.689184 -2363.687726 -2703.780494
## 82 83 84 85 86
## -3942.793315 -550.429705 2303.025233 3754.764105 473.431966
## 87 88 89 90 91
## -487.138291 193.921173 4299.783368 -1160.978121 1151.610726
## 92 93 94 95 96
## -2062.722649 -1044.584028 176.438150 273.993805 -7484.430395
## 97 98 99 100 101
## 2384.821783 -8606.280765 -2951.313868 -4051.146214 -1750.171226
## 102 103 104 105 106
## -1274.278501 3168.835333 -2349.651700 2585.450522 -1163.488050
## 107 108 109 110 111
## 965.100852 2583.361967 -3155.284293 -4726.530608 -857.343117
## 112 113 114 115 116
## 1896.723775 11689.403371 -1235.801837 2673.446294 4269.609697
## 117 118 119 120 121
## 3512.503023 -1088.104992 -4706.808615 -3719.790107 2320.404180
## 122 123 124 125 126
## -1729.883608 1341.464907 8860.494186 857.064452 140.051926
## 127 128 129 130 131
## -2512.612466 2660.540079 7059.801534 1025.197377 -8487.225374
## 132 133 134 135 136
## 1752.420361 4139.987235 -3156.270498 -1415.714772 -851.610565
## 137 138 139 140 141
## -3878.576204 1180.988988 -496.105512 -2914.475787 1714.940536
## 142 143 144 145 146
## -1882.272109 -7831.905680 2030.369652 -3485.767457 2093.929776
## 147 148 149 150 151
## -262.834584 1018.148333 -362.634094 1348.996119 1185.062049
## 152 153 154 155 156
## 3356.285979 -4859.043693 -1176.299973 -3238.377353 5951.691074
## 157 158 159 160 161
## 9747.554761 -3659.891392 -5007.798058 3372.156247 -31.954733
## 162 163 164 165 166
## 2470.811792 -6132.835774 -6974.137878 3925.610404 17165.982073
## 167 168 169 170 171
## 3408.231540 -617.676800 -2666.669383 -1327.160497 3366.115109
## 172 173 174 175 176
## -451.589061 -8299.842648 2634.436753 4097.324253 398.919346
## 177 178 179 180 181
## 8523.326997 -9472.474810 -3701.138094 -10976.075158 -11476.740673
## 182 183 184 185 186
## 993.602458 9053.674905 -1664.968637 5692.896075 6321.006644
## 187 188 189 190 191
## 12923.890038 8196.383242 -4300.387899 2221.643756 10121.926025
## 192 193 194 195 196
## -1892.390223 -2697.208610 -10536.232280 -6621.965132 973.865710
## 197 198 199 200 201
## -5492.223160 -10054.789861 5125.124362 -3323.962047 -1967.113702
## 202 203 204 205 206
## -1057.779788 6241.520540 9628.296034 321.723888 2666.270024
## 207 208 209 210 211
## 2838.182807 5523.484745 12571.713352 -5950.733407 -11559.756178
## 212 213 214 215 216
## -5929.073378 -10848.631737 -5334.278659 1270.009073 -13265.465737
## 217 218 219 220 221
## 16136.883160 7556.356892 1273.476644 26432.393207 12263.719267
## 222 223 224 225 226
## 7067.468359 13755.740884 -4188.637639 -2016.464213 3502.041110
## 227 228 229 230 231
## 84.937957 2472.599650 8733.048409 5561.298234 -2171.808645
## 232 233 234 235 236
## -2091.297833 9164.029546 -11769.947553 -7543.758040 -8800.516116
## 237 238 239 240 241
## -10359.621601 2821.090104 1096.612679 -8551.651468 -9242.397495
## 242 243 244 245 246
## 8843.866041 -8016.601148 2237.478275 -10550.984540 -4302.093761
## 247 248 249 250 251
## 1175.358781 755.388610 -12564.097998 3396.982567 1814.950910
## 252 253 254 255 256
## 3962.953896 1883.794279 -1413.336716 10885.418681 20621.638351
## 257 258 259 260 261
## 2925.859623 -4546.759571 3833.839384 -1973.023137 3458.776799
## 262 263 264 265 266
## -5131.992183 -11171.935384 -5002.184084 -792.809275 -5458.774664
## 267 268 269 270 271
## 8509.860668 -4553.949962 3917.028121 -2382.592646 4155.420253
## 272 273 274 275 276
## 429.967535 7022.458484 -1698.598365 11738.247548 -4881.886021
## 277 278 279 280 281
## 1429.193886 -670.537873 7553.153927 -5362.271305 -3030.886463
## 282 283 284 285 286
## -11557.037062 -2952.463519 18375.734713 7486.842639 2429.606911
## 287 288 289 290 291
## -935.685450 600.072893 6092.023618 6570.198442 -19090.825026
## 292 293 294 295 296
## -11432.027765 -8397.198115 9402.426523 2799.766382 -1453.920941
## 297 298 299 300 301
## 27129.610969 9757.238917 4580.564087 9193.404730 2522.250691
## 302 303 304 305 306
## -1366.666073 7567.940321 -24630.300227 -3827.851874 -458.330905
## 307 308 309 310 311
## -7246.915248 -4236.138289 2677.195560 -9449.184009 -3469.785001
## 312 313 314 315 316
## -8418.400951 1348.303008 -3370.573429 1833.476175 -4302.431328
## 317 318 319 320 321
## 27230.070284 -1008.406218 3007.396932 10540.729306 5281.391464
## 322 323 324 325 326
## 32065.543414 4745.609019 -21301.256095 1474.517976 795.350355
## 327 328 329 330 331
## -6776.331079 -2028.291330 -33553.790305 696.899216 -2484.797521
## 332 333 334 335 336
## -268.286087 -3341.080402 3919.313458 -612.161237 -7127.259381
## 337 338 339 340 341
## -3277.617502 -2347.614109 -7832.758762 3712.124610 -1523.029516
## 342 343 344 345 346
## -1890.091839 -1145.779668 23.485471 324.579653 -1780.365240
## 347 348 349 350 351
## -9608.778329 -13356.015986 2187.547952 -4457.075719 -3788.200042
## 352 353 354 355 356
## -6107.123155 1630.840957 1253.077531 2611.236286 -3922.272154
## 357 358 359 360 361
## -669.872526 518.755074 6847.564175 87.968865 -231.362231
## 362 363 364 365 366
## 2386.832518 -2955.406192 -1076.509019 -8940.885947 -4800.408592
## 367 368 369 370 371
## -6375.640784 -5098.231218 -7391.380595 4890.834320 226.197603
## 372 373 374 375 376
## 6967.896226 -7812.941642 -2421.786253 -3543.781936 -2616.998926
## 377 378 379 380 381
## -12604.397793 1785.058871 -10763.879063 5588.584509 9204.024106
## 382 383 384 385 386
## 2959.963936 -2579.487863 1425.898466 6556.215066 11199.105938
## 387 388 389 390 391
## -6050.973606 -5599.719052 -382.469927 8336.490850 1561.340519
## 392 393 394 395 396
## 10961.908893 -10171.991588 2508.266967 438.928645 287.648461
## 397 398 399 400 401
## -928.724097 -834.418345 -14755.154629 8305.369140 -1418.461199
## 402 403 404 405 406
## -1603.021275 6758.015755 -8175.875955 -1512.215018 -2739.427633
## 407 408 409 410 411
## -6015.860779 -3034.731251 -4082.695678 -8908.790687 6005.459941
## 412 413 414 415 416
## 1488.217045 -7535.060883 -7833.986360 14099.025548 3639.261220
## 417 418 419 420 421
## 4295.314398 -8252.341854 -4935.784311 -2777.383433 2651.426325
## 422 423 424 425 426
## -14190.100227 -2927.317091 -9229.140839 2907.198378 6854.552088
## 427 428 429 430 431
## 6422.767910 -4167.418442 -4290.839172 -4881.573137 -1936.899015
## 432 433 434 435 436
## -5857.308268 -6758.289312 -6065.793343 -1497.472649 -955.324684
## 437 438 439 440 441
## -5087.704168 2477.006027 4718.224884 -5200.847304 -2292.031394
## 442 443 444 445 446
## 1443.482414 -3980.854637 2698.751678 -6729.278827 -12245.621173
## 447 448 449 450 451
## -4614.969371 9548.046542 -2165.591409 4622.078411 -6020.921599
## 452 453 454 455 456
## -1259.783043 245.942895 2883.125275 -12424.373025 3245.805393
## 457 458 459 460 461
## -6839.569459 6399.026161 2864.899180 2347.285884 -4015.470470
## 462 463 464 465 466
## 1932.451745 -176.006906 1622.598260 -698.382166 3173.670837
## 467 468 469 470 471
## -2826.636895 5625.818987 -7137.896235 -3137.454894 -2368.593447
## 472 473 474 475 476
## -4820.140615 2853.191677 7643.640903 -6194.577191 1324.501797
## 477 478 479 480 481
## -6343.279416 -2991.409639 1871.621077 -13077.950673 -9870.136317
## 482 483 484 485 486
## -1296.101362 -77.004704 -1066.115409 -1449.351164 -9694.549934
## 487 488 489 490 491
## 11004.870245 6107.795819 7271.449910 -5608.437186 5209.319684
## 492 493 494 495 496
## 9118.584752 5853.433209 -13689.075139 -10741.248961 -3586.572844
## 497 498 499 500 501
## -1242.903472 -660.169137 -7762.355579 493.419395 4165.277865
## 502 503 504 505 506
## 5371.694774 506.410165 -76.202567 -7396.689145 431.055291
## 507 508 509 510 511
## -5190.430695 1702.210219 -1434.064021 -8294.128389 -719.368808
## 512 513 514 515 516
## -2793.959645 -703.960622 1213.459317 -9621.322084 -7870.523773
## 517 518 519 520 521
## 24195.568327 9664.647610 5691.448063 -5538.545019 2611.233830
## 522 523 524 525 526
## 16825.389511 11235.990205 -24412.601710 -5252.873419 -3910.756517
## 527 528 529 530 531
## 4405.617569 -535.467155 -11279.857625 4244.325698 13749.540382
## 532 533 534 535 536
## -5174.598944 4190.293209 5360.156103 -1998.985708 -4742.347313
## 537 538 539 540 541
## -7260.847467 -2266.388165 8163.070153 -54.233613 -8321.971104
## 542 543 544 545 546
## 1658.374315 -762.131374 205.366368 -11192.837695 -11194.652524
## 547 548 549 550 551
## 1934.324412 6887.258231 -1456.082280 699.666129 -7862.448126
## 552 553 554 555 556
## 8441.387230 757.307343 -12097.761529 9038.172774 8505.047262
## 557 558 559 560 561
## -79.219420 4673.963965 -3766.785829 13926.147350 21278.631337
## 562 563 564 565 566
## -6741.804063 -9931.930337 6555.447092 -7.791896 3224.918008
## 567 568 569 570 571
## -7611.308380 -17516.125145 6506.764267 6263.087454 1716.156471
## 572 573 574 575 576
## 2910.224437 1576.883690 -2359.271152 14531.828969 -9871.118281
## 577 578 579 580 581
## -6437.775553 8536.769426 2662.344450 -6746.286578 7328.474213
## 582 583 584 585 586
## -3997.881456 -2960.457040 15527.448042 -14713.828310 8254.633802
## 587 588 589 590 591
## -123.368527 -6402.977338 -925.112714 85.231828 -10818.753365
## 592 593 594 595 596
## 1660.466465 -7285.798649 2944.327474 8733.425216 -7657.714184
## 597 598 599 600 601
## 5713.415376 2580.624386 6694.253740 -3368.739765 5980.928477
## 602 603 604 605 606
## -8481.209979 2096.220283 1106.288642 2972.798962 1323.440986
## 607 608 609 610 611
## 224.094356 -5982.428340 7919.806609 -1357.458428 -2741.955213
## 612 613 614 615 616
## -3611.960378 -8376.147332 11832.265561 4780.456406 -9480.871173
## 617 618 619 620 621
## 11484.871934 5881.118739 -5753.357180 26197.132412 -13045.000344
## 622 623 624 625 626
## -6959.569931 2998.387058 -4322.376726 -10742.643735 11172.241303
## 627 628 629 630 631
## -21785.516501 -2515.965494 8576.987226 11015.658374 -1697.795611
## 632 633 634 635 636
## 33143.599308 -6798.740579 5527.164166 5202.318057 -2470.781151
## 637 638 639 640 641
## -5535.541878 -2112.995060 -12595.477972 -2377.151030 -2015.520586
## 642 643 644 645 646
## -2645.339923 -2977.848744 1700.588506 4311.430231 16835.145227
## 647 648 649 650 651
## 18387.312504 680.677535 4589.770521 10407.959401 19931.394274
## 652 653 654 655 656
## 496.675370 -28298.669066 -1507.748777 -2447.873254 1722.638594
## 657 658 659 660 661
## -3335.408436 -10753.869748 1555.717780 4115.862595 -1123.439837
## 662 663 664 665 666
## 12918.903491 1225.698675 1679.335877 -11825.402920 1266.878639
## 667 668 669 670 671
## 1074.224747 -5279.330995 -7513.132493 1976.792519 -3804.566327
## 672 673 674 675 676
## 2586.266578 -3470.783542 -9423.888212 -8381.969955 -3046.804029
## 677 678 679 680 681
## 102.547168 2772.453599 631.548675 -3911.476746 -1890.480837
## 682 683 684 685 686
## -1400.335358 -8325.194780 4573.629948 -2326.326468 -1481.577333
## 687 688 689 690 691
## 503.671347 10767.104009 9748.650074 10511.816773 -9783.524467
## 692 693 694 695 696
## -3658.398817 -3238.017114 5777.305325 -10484.915504 -7997.899114
## 697 698 699 700 701
## -8689.374910 -6345.382135 -4806.948586 3015.501528 -4474.718667
## 702 703 704 705 706
## -1969.605529 4149.818436 31028.471017 9440.356895 23372.975596
## 707 708 709 710 711
## 1625.601509 8274.439598 22880.694021 6539.722179 -18216.075605
## 712 713 714 715 716
## 4802.562609 -5459.627816 -121.084794 457.726992 -17289.915127
## 717 718 719 720 721
## -5300.256294 3295.334326 -3050.381878 -13017.076425 4233.205916
## 722 723 724 725 726
## -5598.814725 697.052052 -3978.634938 -12492.875621 1315.164169
## 727 728 729 730 731
## -1916.834396 -9827.023412 17214.706898 1733.733038 -2762.733721
## 732 733 734 735 736
## 5673.832355 -8667.840875 -766.020383 8095.919427 -15388.173136
## 737 738 739 740 741
## -5957.343268 7359.144597 -4827.620357 115.320498 1783.185903
## 742 743 744 745 746
## -1998.485987 -5211.348719 6366.745379 -6315.890611 22654.265804
## 747 748 749 750 751
## 7798.454868 -1971.191958 -7315.014371 23383.844465 -4307.249912
## 752 753 754 755 756
## 1375.857949 -14442.788241 56076.863618 26935.572893 15131.472284
## 757 758 759 760 761
## -10600.559673 10638.375885 7350.607201 5846.707239 -46352.255543
## 762 763 764 765 766
## -16179.437552 943.003152 -2539.127072 -3480.921654 122818.740096
## 767 768 769 770 771
## 19412.978388 43830.101859 22728.449390 12225.535090 15990.682226
## 772 773 774 775 776
## 25869.375772 -98657.800436 -6730.549490 -35816.772212 1710.944420
## 777 778 779 780 781
## -1251.109438 3374.097011 -7431.480019 -1468.537869 -1968.418407
## 782 783 784 785 786
## 3401.117392 -7172.784718 -2260.607675 3895.385915 2247.854471
## 787 788 789 790 791
## -2772.439766 -4062.490892 1720.926545 2839.165144 -61.115936
## 792 793 794 795 796
## -6712.220491 -5797.772443 -1167.394381 -1282.447287 -7836.077448
## 797 798 799 800 801
## -2379.417710 -3293.669396 -2692.045014 10697.623108 2228.887542
## 802 803 804 805 806
## 7070.889259 2923.052694 -5445.704002 8183.739532 9879.998063
## 807 808 809 810 811
## -10587.006400 -7394.234182 -7510.983464 2992.888879 4185.965358
## 812 813 814 815 816
## -2272.485708 -14169.861333 -4129.640599 6238.445356 8225.033534
## 817 818 819 820 821
## -9675.145042 -7761.605755 -9354.270712 9728.485617 -1235.351598
## 822 823 824 825 826
## -4383.962149 -8462.143954 7852.668686 7902.664621 4973.436191
## 827 828 829 830 831
## -3099.324033 -712.486619 2890.595363 4669.950460 1629.911507
## 832 833 834 835 836
## -6687.834424 2087.303534 -397.911297 2752.953802 4142.552526
## 837 838 839 840 841
## -1131.189989 -9331.654882 5670.137205 -4861.408751 -7582.398878
## 842 843 844 845 846
## 3011.060329 -13050.361373 -2836.913068 11610.036494 1306.077351
## 847 848 849 850 851
## 631.931302 -663.752979 4510.027660 12687.846338 -3676.230770
## 852 853 854 855 856
## -11559.210367 -1215.768813 5331.085801 7548.413247 -5829.161827
## 857 858 859 860
## -5667.645583 1573.638980 -2264.368386 2499.552577
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17255.89 20100.98 24351.85 24070.02 26421.66 23756.27 24472.06 19707.67
## 10 11 12 13 14 15 16 17
## 19444.42 16789.13 17566.02 14297.82 14348.94 15013.11 16708.44 15029.70
## 18 19 20 21 22 23 24 25
## 16063.61 15437.60 22514.85 21599.53 21079.77 22968.39 22294.87 22946.94
## 26 27 28 29 30 31 32 33
## 24791.55 18724.39 20449.27 28280.85 28338.47 28011.62 25642.77 27044.18
## 34 35 36 37 38 39 40 41
## 30884.40 31232.57 32640.71 30153.14 34133.64 37339.37 34397.63 31210.85
## 42 43 44 45 46 47 48 49
## 30059.47 20645.92 28159.13 30593.83 31682.23 38515.31 38010.27 42669.04
## 50 51 52 53 54 55 56 57
## 46902.19 39605.47 34177.91 29204.26 22353.72 28639.11 25222.26 21522.58
## 58 59 60 61 62 63 64 65
## 25927.70 27186.33 27483.12 27894.64 23768.60 40347.70 42190.30 37439.75
## 66 67 68 69 70 71 72 73
## 41643.61 46555.64 57215.98 55231.21 40485.24 37992.94 41008.62 35312.95
## 74 75 76 77 78 79 80 81
## 30768.53 21478.81 24677.93 20606.64 22691.17 17590.45 19604.40 18831.49
## 82 83 84 85 86 87 88 89
## 17859.94 15930.29 17207.12 20812.52 25227.00 26216.14 26241.08 26857.36
## 90 91 92 93 94 95 96 97
## 30979.41 29810.82 30809.44 28875.30 28075.70 28443.58 28849.86 22432.04
## 98 99 100 101 102 103 104 105
## 25444.85 18480.46 17337.43 15379.60 15679.14 16356.02 20825.37 19909.55
## 106 107 108 109 110 111 112 113
## 23418.06 23208.18 24883.07 27757.71 25257.67 21703.77 21978.99 24623.31
## 114 115 116 117 118 119 120 121
## 35479.80 33673.98 35510.10 38506.21 40460.68 38150.81 32975.65 29319.74
## 122 123 124 125 126 127 128 129
## 31401.03 29682.25 30862.93 38457.08 38099.81 37162.04 34027.89 35807.77
## 130 131 132 133 134 135 136 137
## 41201.66 40642.37 31850.58 33114.44 36301.84 32715.14 31103.61 30189.29
## 138 139 140 141 142 143 144 145
## 26748.87 28162.25 27932.05 25620.06 27642.99 26268.76 19875.63 22903.91
## 146 147 148 149 150 151 152 153
## 20732.21 23707.12 24246.71 25835.92 26017.86 27670.80 28970.57 32000.47
## 154 155 156 157 158 159 160 161
## 27474.01 26737.52 24294.59 30184.30 41680.32 40011.80 37378.70 42395.24
## 162 163 164 165 166 167 168 169
## 43802.76 47216.12 42685.42 37996.10 43417.30 59707.34 61917.82 60333.10
## 170 171 172 173 174 175 176 177
## 57161.16 55561.60 58262.16 57286.99 49584.85 52406.25 56146.08 56182.24
## 178 179 180 181 182 183 184 185
## 63305.76 53815.14 50568.50 41384.03 32929.68 36435.33 46531.25 45987.68
## 186 187 188 189 190 191 192 193
## 51935.99 57676.68 68451.62 73730.53 67429.93 67623.22 74688.25 70367.92
## 194 195 196 197 198 199 200 201
## 65894.09 55145.97 49180.56 50603.79 46201.79 38376.45 44796.39 43025.11
## 202 203 204 205 206 207 208 209
## 42663.35 43141.34 49930.28 58812.85 58442.73 60166.25 61820.80 65609.14
## 210 211 212 213 214 215 216 217
## 75068.59 67157.33 55355.22 49968.06 40971.14 37931.13 41042.47 31070.12
## 218 219 220 221 222 223 224 225
## 48030.93 55346.24 56247.46 78995.85 86485.25 88486.97 96072.64 87030.32
## 226 227 228 229 230 231 232 233
## 81033.24 80615.49 77267.97 76430.09 81163.56 82526.81 76966.44 72182.97
## 234 235 236 237 238 239 240 241
## 77832.38 64490.19 56532.66 48489.34 40107.20 44295.96 46447.08 39902.68
## 242 243 244 245 246 247 248 249
## 33586.99 43861.74 38112.95 42045.70 34315.38 33022.21 36674.75 39496.53
## 250 251 252 253 254 255 256 257
## 30332.87 36266.48 40065.05 45255.92 47972.19 47465.15 57758.36 75242.43
## 258 259 260 261 262 263 264 265
## 75057.62 68373.30 69854.02 66077.65 67522.71 61285.08 50567.76 46598.09
## 266 267 268 269 270 271 272 273
## 46807.35 42917.00 51714.52 47990.40 52134.02 50252.01 54316.32 54612.11
## 274 275 276 277 278 279 280 281
## 60625.03 58261.04 67926.74 61856.09 62065.97 60416.27 66154.84 59890.03
## 282 283 284 285 286 287 288 289
## 56456.47 46016.61 44414.55 61633.87 67159.82 67568.97 64988.50 64076.55
## 290 291 292 293 294 295 296 297
## 68074.52 71981.83 52992.60 43102.06 37117.57 47431.23 50670.64 49785.25
## 298 299 300 301 302 303 304 305
## 73963.48 79904.44 80571.60 85180.61 83380.52 78414.49 81878.73 56796.28
## 306 307 308 309 310 311 312 313
## 53060.19 52740.20 46535.00 43746.52 47347.18 39904.93 38627.97 33193.55
## 314 315 316 317 318 319 320 321
## 36975.29 36157.24 39985.86 37971.79 63738.98 61581.75 63204.13 71196.32
## 322 323 324 325 326 327 328 329
## 73581.89 99044.68 97423.54 73271.62 72070.36 70428.90 62386.58 59510.93
## 330 331 332 333 334 335 336 337
## 29481.53 33166.37 33605.57 35923.79 35265.12 41027.88 42102.69 37353.76
## 338 339 340 341 342 343 344 345
## 36568.76 36695.33 32017.73 38012.32 38675.23 38933.49 39808.66 41593.28
## 346 347 348 349 350 351 352 353
## 43413.94 43165.78 36115.59 26690.31 32031.08 30892.91 30483.27 28101.44
## 354 355 356 357 358 359 360 361
## 32776.92 36528.48 40988.84 39179.16 40438.53 42575.44 49965.32 50515.51
## 362 363 364 365 366 367 368 369
## 50717.02 53178.41 50663.65 50108.60 42759.12 39957.93 36137.66 33917.95
## 370 371 372 373 374 375 376 377
## 29978.59 37261.23 39546.53 47426.37 41402.36 40849.92 39388.28 38921.40
## 378 379 380 381 382 383 384 385
## 29795.66 34390.45 27447.13 35660.55 45986.18 49549.06 47823.67 49813.93
## 386 387 388 389 390 391 392 393
## 56029.61 65508.26 58724.43 53196.61 52925.51 60299.80 60822.81 69485.28
## 394 395 396 397 398 399 400 401
## 58598.73 60164.50 59724.92 59209.15 57697.13 56459.58 43227.63 51807.18
## 402 403 404 405 406 407 408 409
## 50808.31 49775.27 56172.02 48719.79 48031.43 46359.29 42039.59 40871.12
## 410 411 412 413 414 415 416 417
## 38936.36 33034.68 40901.93 43826.20 38502.27 33593.97 48455.17 52297.26
## 418 419 420 421 422 423 424 425
## 56223.77 48698.21 45024.10 43701.00 47284.96 35712.17 35441.57 29704.37
## 426 427 428 429 430 431 432 433
## 35290.31 43612.09 50499.42 47267.12 44337.86 41265.18 41153.45 37633.72
## 434 435 436 437 438 439 440 441
## 33774.79 31010.76 32585.75 34433.85 32439.85 37302.63 43503.85 40258.46
## 442 443 444 445 446 447 448 449
## 39964.66 42969.00 40856.53 44843.28 40093.48 31131.97 29970.24 41319.31
## 450 451 452 453 454 455 456 457
## 41001.06 46648.35 42287.50 42636.91 44256.30 47971.94 37853.19 42699.14
## 458 459 460 461 462 463 464 465
## 38125.55 45689.39 49207.00 51825.76 48557.55 50896.72 51098.12 52843.95
## 466 467 468 469 470 471 472 473
## 52341.90 55283.64 52613.75 57661.47 50926.03 48538.59 47125.71 43752.38
## 474 475 476 477 478 479 480 481
## 47505.93 54964.15 49394.93 51096.99 45889.41 44269.52 47100.52 36521.99
## 482 483 484 485 486 487 488 489
## 30087.96 31956.00 34650.83 36139.78 37104.98 30750.13 43271.78 49927.41
## 490 491 492 493 494 495 496 497
## 56753.01 51468.11 56297.84 63926.28 67735.08 54000.82 44585.14 42611.47
## 498 499 500 501 502 503 504 505
## 42934.45 43725.07 38215.58 40612.86 45910.73 51588.45 52297.63 52408.12
## 506 507 508 509 510 511 512 513
## 46114.37 47453.43 43715.22 46468.78 46134.70 39854.80 40985.10 40160.82
## 514 515 516 517 518 519 520 521
## 41265.68 43903.89 36748.95 32031.57 55904.78 64059.84 67710.26 61093.91
## 522 523 524 525 526 527 528 529
## 62432.47 76008.72 82980.60 57948.16 52821.76 49518.38 53894.32 53401.00
## 530 531 532 533 534 535 536 537
## 43591.39 48579.75 61231.46 55756.14 59151.42 63136.41 60191.06 55225.28
## 538 539 540 541 542 543 544 545
## 48692.10 47348.93 55280.52 55031.11 47596.34 49818.42 49645.21 50338.55
## 546 547 548 549 550 551 552 553
## 40994.08 32835.53 37174.31 45285.23 45082.33 46787.02 40801.04 49807.69
## 554 555 556 557 558 559 560 561
## 50962.19 40748.54 50282.81 58140.08 57505.46 61100.64 56870.85 68623.08
## 562 563 564 565 566 567 568 569
## 85299.95 75397.93 63969.55 68385.65 66511.37 67697.17 59273.13 43273.52
## 570 571 572 573 574 575 576 577
## 50277.20 56178.13 57360.06 59434.12 60080.70 57209.17 69447.12 58828.06
## 578 579 580 581 582 583 584 585
## 52555.52 60151.66 61654.57 54753.53 61015.60 56594.89 53641.55 67201.97
## 586 587 588 589 590 591 592 593
## 52640.94 59979.94 59072.98 52799.68 52105.34 52381.18 43103.68 45898.51
## 594 595 596 597 598 599 600 601
## 40528.82 44771.57 53528.57 46864.58 52719.38 55095.46 60760.45 56921.36
## 602 603 604 605 606 607 608 609
## 61731.64 53306.35 55185.00 55960.77 58267.27 58840.91 58382.00 52563.62
## 610 611 612 613 614 615 616 617
## 59620.17 57681.67 54780.96 51489.43 44457.45 55959.40 59844.01 50785.99
## 618 619 620 621 622 623 624 625
## 61180.45 65362.36 58856.87 81068.29 66201.86 58536.76 60538.23 55894.93
## 626 627 628 629 630 631 632 633
## 46237.33 56936.95 37507.39 37367.73 46929.06 57404.08 55450.11 84158.17
## 634 635 636 637 638 639 640 641
## 74351.55 76550.68 78186.78 72916.97 65641.57 62278.34 50192.15 48561.66
## 642 643 644 645 646 647 648 649
## 47454.05 45937.42 44323.27 46998.14 51612.14 66571.97 80985.61 78111.09
## 650 651 652 653 654 655 656 657
## 79014.18 84881.32 98316.04 93078.53 63370.61 60824.30 57780.93 58764.84
## 658 659 660 661 662 663 664 665
## 55208.44 45628.28 48010.85 52325.44 51518.24 63071.44 62949.24 63238.55
## 666 667 668 669 670 671 672 673
## 51702.55 53061.06 54078.76 49420.99 43405.21 46437.85 44038.45 47522.64
## 674 675 676 677 678 679 680 681
## 45276.75 38119.68 32781.66 32779.17 35526.12 40254.59 42513.33 40519.34
## 682 683 684 685 686 687 688 689
## 40542.91 40991.34 35337.94 41662.61 41160.43 41459.47 43453.47 54153.21
## 690 691 692 693 694 695 696 697
## 62604.18 70647.38 59952.26 55963.02 52847.69 57997.92 48298.04 42001.80
## 698 699 700 701 702 703 704 705
## 35902.10 32623.66 31104.78 36607.29 34872.18 35544.32 41472.81 70110.79
## 706 707 708 709 710 711 712 713
## 76264.74 93798.68 90120.70 92714.02 107727.85 106569.36 83948.29 84295.34
## 714 715 716 717 718 719 720 721
## 75640.23 72745.13 70723.20 53465.97 48867.81 52357.24 49863.93 38987.37
## 722 723 724 725 726 727 728 729
## 44551.10 40825.23 43068.63 40945.45 31659.84 35607.55 36232.31 29872.72
## 730 731 732 733 734 735 736 737
## 47926.55 50172.45 48207.88 53857.41 46269.88 46544.22 54519.46 40981.49
## 738 739 740 741 742 743 744 745
## 37396.28 45890.91 42667.97 44169.39 46935.91 46049.78 42471.68 49455.03
## 746 747 748 749 750 751 752 753
## 44480.02 65425.83 70741.91 66854.30 58796.01 78559.39 71639.14 70559.22
## 754 755 756 757 758 759 760 761
## 55808.14 104489.57 121546.53 126131.85 107672.48 110098.82 109346.86 107377.68
## 762 763 764 765 766 767 768 769
## 60093.29 45156.28 47063.98 45689.64 43667.83 152152.31 156585.61 181769.69
## 770 771 772 773 774 775 776 777
## 185333.32 179275.89 177274.91 184151.51 81452.12 72048.92 38450.77 41880.97
## 778 779 780 781 782 783 784 785
## 42289.62 46683.77 41087.11 41406.85 41249.60 45799.50 40541.04 40238.76
## 786 787 788 789 790 791 792 793
## 45348.57 48370.87 46626.78 43978.22 46714.69 50079.54 50485.08 45033.20
## 794 795 796 797 798 799 800 801
## 41072.39 41656.88 42066.65 36703.56 36785.24 36058.47 35949.23 47541.97
## 802 803 804 805 806 807 808 809
## 50268.97 56876.09 59022.85 53591.55 60747.86 68475.43 57354.95 50434.70
## 810 811 812 813 814 815 816 817
## 44291.97 48098.89 52463.49 50635.72 38654.78 36960.70 44532.40 52876.00
## 818 819 820 821 822 823 824 825
## 44533.89 38922.27 32633.51 43801.64 43979.96 41387.14 35563.90 44722.19
## 826 827 828 829 830 831 832 833
## 52760.28 57219.90 54065.92 53396.26 55956.91 59745.37 60398.69 53708.27
## 834 835 836 837 838 839 840 841
## 55528.05 54947.19 57190.59 60361.90 58526.65 49766.29 55214.55 50777.26
## 842 843 844 845 846 847 848 849
## 44528.65 48321.36 37611.77 37178.68 49411.64 51095.50 51976.90 51615.26
## 850 851 852 853 854 855 856 857
## 55815.87 66621.23 61768.92 50652.05 49976.91 55102.44 61512.16 55429.79
## 858 859 860
## 50261.36 52070.80 50300.02
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8088
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 5.312009 0.8284048 4.117924
## t2* 2736.418563 164.4655089 894.598139
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.188825 5.259271 14.00103
## 2 lag_depvar 1661.886281 2776.360918 4561.47102Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Sep 8 00:59:12 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Sep 8 00:59:22 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Sep 8 00:59:31 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Sep 8 00:59:41 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Sep 8 00:59:51 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Sep 8 01:00:01 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Sep 8 01:00:11 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Sep 8 01:00:21 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Sep 8 01:00:30 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Sep 8 01:00:40 2025
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_25<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2025",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2020",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_25 %>%
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))| Item | 2025 | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|---|
| Agua | 7.180125 | 6.993667 | 5.195333 | 5.410333 | 5.849167 | 9.93775 |
| Comida | 223.381125 | 326.890000 | 366.009167 | 312.386750 | 317.896583 | 392.93367 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electricidad | 55.862500 | 83.582750 | 38.104750 | 47.072333 | 29.523000 | 20.60458 |
| Enceres | 3.335000 | 23.989000 | 18.259750 | 24.219750 | 14.801167 | 39.01200 |
| Farmacia | 0.000000 | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 14.03675 |
| Gas/Bencina | 23.106250 | 44.292667 | 42.636000 | 45.575000 | 13.583667 | 17.25833 |
| Diosi | 16.561125 | 33.319583 | 55.804250 | 31.180667 | 52.687833 | 37.12133 |
| donaciones/regalos | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electrodomésticos/ Mantención casa | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| VTR | 16.497500 | 18.326667 | 12.829167 | 25.156667 | 19.086917 | 19.11375 |
| Netflix | 0.000000 | 1.391417 | 8.713833 | 7.151583 | 7.028750 | 8.24725 |
| Otros | 0.000000 | 76.164000 | 5.481667 | 5.000000 | 0.000000 | 0.00000 |
| Total | 345.923625 | 614.949750 | 563.738000 | 505.988083 | 474.453167 | 558.26542 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")
tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 54 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2808, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)# Configurar API Key
nixtlar::nixtla_set_api_key(Sys.getenv("API_NIXTLA"))## API key has been set for the current session.try(nixtlar::nixtla_set_api_key(Sys.getenv("NIXTLA")))## API key has been set for the current session.# Preparar datos en formato requerido por TimeGPT
uf_timegpt <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y)
# Realizar pronóstico con TimeGPT
timegpt_fcst <- nixtlar::nixtla_client_forecast(
uf_timegpt,
h = 298, # 298 días a pronosticar
freq = "D", # Frecuencia diaria
add_history = TRUE, # Incluir datos históricos en el output
level = c(80,95),
model= "timegpt-1-long-horizon",
clean_ex_first = TRUE
)## The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.# 1. Convertir 'ds' a fecha en ambas tablas
uf_timegpt <- uf_timegpt %>%
mutate(ds = as.Date(ds))
timegpt_fcst <- timegpt_fcst %>%
mutate(ds = as.Date(ds))
# 2. Combinar los datos históricos y el pronóstico
full_data <- bind_rows(
uf_timegpt %>% mutate(type = "Histórico"),
timegpt_fcst %>% mutate(type = "Pronóstico")
)
# Visualizar resultados
ggplot(full_data, aes(x = ds, y = TimeGPT)) +
# Intervalo de confianza del 95%
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
# Intervalo de confianza del 80%
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
# Línea histórica
geom_line(data = filter(full_data, type == "Histórico"),
aes(color = "Histórico"), size = 1) +
# Línea de pronóstico
geom_line(data = filter(full_data, type == "Pronóstico"),
aes(color = "Pronóstico"), size = 1) +
# Línea vertical separadora
geom_vline(xintercept = max(filter(full_data, type == "Histórico")$ds),
linetype = "dashed", color = "red", size = 0.8) +
# Configuración del eje x
scale_x_date(
date_breaks = "3 months", # Reduce la frecuencia de las etiquetas
date_labels = "%b %Y", # Formato de etiquetas (mes y año)
) +
# Configuración del eje y
scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
# Configuración de colores
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
# Títulos y subtítulos
labs(
title = "Pronóstico de Serie Temporal con TimeGPT",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Valor",
color = "Leyenda"
) +
# Tema y estilos
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)## Warning: Removed 2808 rows containing missing values or values outside the scale range
## (`geom_line()`).
library(prophet)## Warning: package 'prophet' was built under R version 4.4.3## Loading required package: Rcpp## Warning: package 'Rcpp' was built under R version 4.4.3## Loading required package: rlang## Warning: package 'rlang' was built under R version 4.4.3##
## Attaching package: 'rlang'## The following objects are masked from 'package:purrr':
##
## %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
## flatten_raw, invoke, splice## The following object is masked from 'package:sparklyr':
##
## invoke## The following object is masked from 'package:data.table':
##
## := model <- prophet(
cbind.data.frame(ds= as.Date(uf_timegpt$ds), y=uf_timegpt$y),
# Trend flexibility
growth = "linear",
changepoint.prior.scale = 0.05, # Reduced for smoother trend
n.changepoints = 50, # Increased from default 25
# Seasonality
yearly.seasonality = TRUE,
weekly.seasonality = TRUE,
daily.seasonality = FALSE, # Disabled for daily data
seasonality.mode = "additive",
seasonality.prior.scale = 15, # Increased to capture stronger seasonality
# Holidays (if applicable)
# holidays = generated_holidays # Create with add_country_holidays()
# Uncertainty intervals
interval.width = 0.95,
uncertainty.samples = 1000
)
future <- make_future_dataframe(model, periods = 298, include_history = T)
forecast <- predict(model, future)
forecast <- forecast[, c("ds", "yhat", "yhat_lower", "yhat_upper")]
forecast$pred <- ifelse(forecast$ds > max(uf_timegpt$ds), 1,0)## Warning in check_tzones(e1, e2): 'tzone' attributes are inconsistentforecast$ds <- as.Date(forecast$ds)
ggplot(forecast, aes(x = ds, y = yhat)) +
geom_ribbon(aes(ymin = yhat_lower, ymax = yhat_upper),
fill = "#9ecae1", alpha = 0.4) +
geom_line(color = "#08519c", linewidth = 0.8) +
geom_vline(xintercept = max(uf_timegpt$ds), color = "red", linetype = "dashed", linewidth=1) +
scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
scale_y_continuous(labels = scales::comma) +
labs(title = "Valores predichos (95%IC)",
# subtitle = "March 10, 2025 - May 7, 2025",
x = "Fecha",
y = "Valor",
# caption = "Source: Prophet Forecast Model"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
plot.subtitle = element_text(color = "gray50"),
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
plot.caption = element_text(color = "gray30")
)
La proyección de la UF a 298 días más 2025-09-09 00:04:58 sería de: 26.848 pesos// Percentil 95% más alto proyectado: 35.188,75
Según TimeGPT: La proyección de la UF a 298 días más 2026-07-04 sería de: 40.201,26 pesos// Percentil 80% más alto proyectado: 42.239,44 pesos// Percentil 95% más alto proyectado: 42.556,3
Según prophet: La proyección de la UF a 298 días más 2026-07-04 sería de: 42.517 pesos// Percentil 95% más alto proyectado: 49.912
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 26460.95 | 26321.73 |
| Lo.80 | 26594.36 | 26486.94 |
| Point.Forecast | 26848.21 | 26799.01 |
| Hi.80 | 31608.29 | 32186.25 |
| Hi.95 | 34460.82 | 35038.08 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.4483 1029.8941
## s.e. 0.1022 39.3997
##
## sigma^2 = 38941: log likelihood = -528.7
## AIC=1063.4 AICc=1063.72 BIC=1070.51paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.4536 599.6322 13.2153
## s.e. 0.1033 315.9027 9.6172
##
## sigma^2 = 38484: log likelihood = -527.72
## AIC=1063.45 AICc=1063.99 BIC=1072.92forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 623.5974 | 597.1924 | 553.2689 |
| Lo.80 | 772.9295 | 746.9591 | 629.9390 |
| Point.Forecast | 1055.0244 | 1029.8753 | 804.9503 |
| Hi.80 | 1337.1193 | 1312.7914 | 1160.2933 |
| Hi.95 | 1486.4514 | 1462.5581 | 1408.0902 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Gastos_casa_mensual_2022$mes_ano <- gsub("marzo", "Mar", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("abril", "Apr", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("mayo", "May", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("junio", "Jun", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("julio", "Jul", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("agosto", "Aug", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("septiembre", "Sep", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("octubre", "Oct", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("noviembre", "Nov", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("diciembre", "Dec", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("enero", "Jan", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("febrero", "Feb", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022<- dplyr::filter(Gastos_casa_mensual_2022, !is.na(Tami))
Gastos_casa_mensual_2022$mes_ano <- parse_date_time(Gastos_casa_mensual_2022$mes_ano, "%b_%Y")
Gastos_casa_mensual_2022$mes_ano <- as.Date(as.character(Gastos_casa_mensual_2022$mes_ano))
Gastos_casa_mensual_2022_timegpt <- Gastos_casa_mensual_2022 %>%
mutate(value = Tami + Andrés) %>%
rename(ds = mes_ano, y = value) %>%
mutate(#ds= format(ds, "%Y-%m"),
unique_id = "1") %>% #it is only one series
select(unique_id, ds, y)
#Convertir la base de UF a mensual
uf_timegpt_my <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y) %>%
mutate(ds = ymd(ds)) %>% # Convert 'ds' to Date
mutate(month = month(ds), year = year(ds)) %>% # Extract month and year
group_by(month, year) %>% # Group by month and year
summarise(average_y = mean(y))%>% # Calculate average y
mutate(ds = as.Date(paste0(year,"-",month, "-01")))%>%
ungroup()%>%
select(ds, uf=average_y)
Gastos_casa_mensual_2022_timegpt_ex<-
Gastos_casa_mensual_2022_timegpt |>
dplyr::left_join(uf_timegpt_my, by=c("ds"="ds"))
#Historical Exogenous Variables: These should be included in the input data immediately following the id_col, ds, and y columns
gastos_timegpt_fcst <- nixtlar::nixtla_client_forecast(
Gastos_casa_mensual_2022_timegpt_ex,
h = 12,
freq = "M", # Monthly frequency
add_history = TRUE,
level = c(80, 95),
model = "timegpt-1",#"timegpt-1-long-horizon",
clean_ex_first = TRUE
)
# Convert 'ds' to Date format in both tables
Gastos_casa_mensual_2022_timegpt_corr <- Gastos_casa_mensual_2022_timegpt %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
gastos_timegpt_fcst <- gastos_timegpt_fcst %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
# Combine historical and forecast data
full_data_gastos <- bind_rows(
Gastos_casa_mensual_2022_timegpt_corr %>% mutate(type = "Histórico"),
gastos_timegpt_fcst %>% mutate(type = "Pronóstico")
)
full_data_gastos |>
dplyr::mutate(y= ifelse(is.na(y),TimeGPT, y)) |>
# Visualize results
ggplot(aes(x = ds, y = y)) +
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
geom_line(aes(color = type), linewidth = 1.5) +
geom_vline(xintercept = max(filter(full_data_gastos, type == "Histórico")$ds),
linetype = "dashed", color = "red", linewidth = 0.8) +
scale_x_date(
date_breaks = "3 months",
date_labels = "%b %Y"
) +
scale_y_continuous(
name = "Gastos Totales",
labels = scales::comma,
breaks = pretty(full_data_gastos$y, n = 10),
expand = expansion(mult = c(0.05, 0.05))
) +
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
labs(
title = "Pronóstico de Gastos Mensuales (TimeGPT, ajustando por UF promedio mensual)",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Gastos Totales",
color = "Leyenda"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
## system code page: 65001
##
## time zone: UTC
## tzcode source: internal
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] prophet_1.0 rlang_1.1.6 Rcpp_1.1.0
## [4] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [7] Boom_0.9.15 scales_1.4.0 ggiraph_0.9.0
## [10] tidytext_0.4.3 DT_0.34.0 janitor_2.2.1
## [13] autoplotly_0.1.4 rvest_1.0.5 plotly_4.11.0
## [16] xts_0.14.1 forecast_8.24.0 wordcloud_2.6
## [19] RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-16
## [22] NLP_0.3-2 tsibble_1.1.6 lubridate_1.9.4
## [25] forcats_1.0.0 dplyr_1.1.4 purrr_1.1.0
## [28] tidyr_1.3.1 tibble_3.3.0 tidyverse_2.0.0
## [31] gsynth_1.2.1 sjPlot_2.9.0 lattice_0.22-6
## [34] GGally_2.4.0 ggplot2_3.5.2 gridExtra_2.3
## [37] plotrix_3.8-4 sparklyr_1.9.1 httr_1.4.7
## [40] readxl_1.4.5 zoo_1.8-14 stringr_1.5.1
## [43] stringi_1.8.7 DataExplorer_0.8.4 data.table_1.17.8
## [46] reshape2_1.4.4 fUnitRoots_4040.81 plyr_1.8.9
## [49] readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-9 cellranger_1.1.0 httr2_1.2.1
## [4] lifecycle_1.0.4 StanHeaders_2.32.10 doParallel_1.0.17
## [7] globals_0.18.0 vroom_1.6.5 MASS_7.3-60.2
## [10] crosstalk_1.2.2 magrittr_2.0.3 sass_0.4.10
## [13] rmarkdown_2.29 jquerylib_0.1.4 yaml_2.3.10
## [16] fracdiff_1.5-3 doRNG_1.8.6.2 askpass_1.2.1
## [19] pkgbuild_1.4.8 DBI_1.2.3 abind_1.4-8
## [22] quadprog_1.5-8 nnet_7.3-19 rappdirs_0.3.3
## [25] sandwich_3.1-1 inline_0.3.21 data.tree_1.2.0
## [28] tokenizers_0.3.0 listenv_0.9.1 anytime_0.3.12
## [31] spatial_7.3-17 parallelly_1.45.1 codetools_0.2-20
## [34] xml2_1.4.0 tidyselect_1.2.1 farver_2.1.2
## [37] urca_1.3-4 its.analysis_1.6.0 matrixStats_1.5.0
## [40] stats4_4.4.0 jsonlite_2.0.0 ellipsis_0.3.2
## [43] Formula_1.2-5 iterators_1.0.14 systemfonts_1.2.3
## [46] foreach_1.5.2 tools_4.4.0 glue_1.8.0
## [49] xfun_0.53 TTR_0.24.4 ggfortify_0.4.19
## [52] loo_2.8.0 withr_3.0.2 timeSeries_4041.111
## [55] fastmap_1.2.0 boot_1.3-30 openssl_2.3.3
## [58] caTools_1.18.3 digest_0.6.37 timechange_0.3.0
## [61] R6_2.6.1 lfe_3.1.1 colorspace_2.1-1
## [64] networkD3_0.4.1 gtools_3.9.5 generics_0.1.4
## [67] htmlwidgets_1.6.4 ggstats_0.10.0 pkgconfig_2.0.3
## [70] gtable_0.3.6 timeDate_4041.110 lmtest_0.9-40
## [73] S7_0.2.0 selectr_0.4-2 janeaustenr_1.0.0
## [76] htmltools_0.5.8.1 carData_3.0-5 tseries_0.10-58
## [79] snakecase_0.11.1 knitr_1.50 rstudioapi_0.17.1
## [82] tzdb_0.5.0 uuid_1.2-1 nlme_3.1-164
## [85] curl_7.0.0 cachem_1.1.0 KernSmooth_2.23-22
## [88] parallel_4.4.0 fBasics_4041.97 pillar_1.11.0
## [91] vctrs_0.6.5 gplots_3.2.0 slam_0.1-55
## [94] car_3.1-3 dbplyr_2.5.0 xtable_1.8-4
## [97] evaluate_1.0.5 mvtnorm_1.3-3 cli_3.6.5
## [100] compiler_4.4.0 crayon_1.5.3 rngtools_1.5.2
## [103] future.apply_1.20.0 labeling_0.4.3 rstan_2.32.7
## [106] QuickJSR_1.8.0 viridisLite_0.4.2 assertthat_0.2.1
## [109] lazyeval_0.2.2 Matrix_1.7-0 hms_1.1.3
## [112] bit64_4.6.0-1 future_1.67.0 nixtlar_0.6.2
## [115] extraDistr_1.10.0 igraph_2.1.4 RcppParallel_5.1.11-1
## [118] bslib_0.9.0 quantmod_0.4.28 bit_4.6.0#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))